def __init__()

in source/sagemaker/sagemaker_graph_fraud_detection/dgl_fraud_detection/model/pytorch.py [0:0]

• 12 lines of code
• 5 McCabe index (conditional complexity)

    def __init__(self, g, in_size, hidden_size, out_size, n_layers, embedding_size):
        super(HeteroRGCN, self).__init__()
        # Use trainable node embeddings as featureless inputs.
        embed_dict = {ntype: nn.Parameter(torch.Tensor(g.number_of_nodes(ntype), in_size))
                      for ntype in g.ntypes if ntype != 'user'}
        for key, embed in embed_dict.items():
            nn.init.xavier_uniform_(embed)
        self.embed = nn.ParameterDict(embed_dict)
        # create layers
        self.layers = nn.Sequential()
        self.layers.add_module(HeteroRGCNLayer(embedding_size, hidden_size, g.etypes))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.add_module = HeteroRGCNLayer(hidden_size, hidden_size, g.etypes)

        # output layer
        self.layers.add(nn.Dense(hidden_size, out_size))